Application of Reptation Quantum Monte Carlo and Related Methods to LiH

This work investigates mathematical details and computational aspects of Metropolis-Hastings reptation quantum Monte Carlo and its variants, in addition to the Bounce method and its variants. The issues that concern us include the sensitivity of these algorithms' target densities to the position of the trial electron density along the reptile, time-reversal symmetry of the propagators, and the length of the reptile. We calculate the ground-state energy and one-electron properties of LiH at its equilibrium geometry for all these algorithms. The importance sampling is performed with a single-determinant large Slater-type orbitals (STO) basis set. The computer codes were written to exploit the efficiencies engineered into modern, high-performance computing software. Using the Bounce method in the calculation of non-energy-related properties, those represented by operators that do not commute with the Hamiltonian, is a novel work. We found that the unmodified Bounce gives good ground state energy and very good one-electron properties. We attribute this to its favourable time-reversal symmetry in its target density's Green's functions. Breaking this symmetry gives poorer results. Use of a short reptile in the Bounce method does not alter the quality of the results. This suggests that in future applications one can use a shorter reptile to cut down the computational time dramatically.

QUANTIFICATION AND EXTRACTION OF SURFACE FEATURES FROM DIGITAL TERRAIN MODELS

Digital Terrain Models (DTMs) are important in geology and geomorphology, since elevation data contains a lot of information pertaining to geomorphological processes that influence the topography. The first derivative of topography is attitude; the second is curvature. GIS tools were developed for derivation of strike, dip, curvature and curvature orientation from Digital Elevation Models (DEMs). A method for displaying both strike and dip simultaneously as colour-coded visualization (AVA) was implemented. A plug-in for calculating strike and dip via Least Squares Regression was created first using VB.NET. Further research produced a more computationally efficient solution, convolution filtering, which was implemented as Python scripts. These scripts were also used for calculation of curvature and curvature orientation. The application of these tools was demonstrated by performing morphometric studies on datasets from Earth and Mars. The tools show promise, however more work is needed to explore their full potential and possible uses.

The identification, distribution and persistence of oxamyl and its degradation products in planted corn seed, seedling root and soil from oxamyl-treated corn seeds

A simple method was developed for treating corn seeds with oxamyl. It involved soaking the seeds to ensure oxamyl uptake, centrifugation to draw off excess solution, and drying under a stream of air to prevent the formation of fungus. The seeds were found to have an even distribution of oxamyl. Seeds remained fungus-free even 12 months after treatment. The highest nonphytotoxic treatment level was obtained by using a 4.00 mg/mL oxamyl solution. Extraction methods for the determination of oxamyl (methyl-N'N'-dimethyl-N-[(methylcarbamoyl)oxy]-l-thiooxamimidate), its oxime (methyl-N',N'-dimethyl-N-hydroxy-1-thiooxamimidate), and DMCF (N,N-dimethyl-1-cyanoformanade) in seed" root, and soil were developed. Seeds were processed by homogenizing, then shaking in methanol. Significantly more oxamyl was extracted from hydrated seeds as opposed to dry seeds. Soils were extracted by tumbling in methanol; recoveries range~ from 86 - 87% for oxamyl. Root was extracted to 93% efficiency for oxamyl by homogenizing the tissue in methanol. NucharAttaclay column cleanup afforded suitable extracts for analysis by RP-HPLC on a C18 column and UV detection at 254 nm. In the degradation study, oxamyl was found to dissipate from the seed down into the soil. It was also detected in the root. Oxime was detected in both the seed and soil, but not in the root. DMCF was detected in small amounts only in the seed.